Quantitative Data Analysis 101 Tutorial: Descriptive vs Inferential Statistics (With Examples)

Summary

This video from Grad Coach TV demystifies quantitative data analysis, explaining its definition, the distinction between descriptive and inferential statistics, and their applications. It covers essential concepts like mean, median, mode, standard deviation, and skewness, and delves into choosing the right statistical methods based on data type and research questions. Aiming to build confidence in approaching research, the video offers practical advice for students navigating the complex world of quantitative analysis.

Takeaways

• 📊 Quantitative data analysis involves examining numerical data and can be applied to both categorical and numerical data types.
• 🔍 The process is divided into two main branches: descriptive statistics, which describe the sample, and inferential statistics, which make predictions about the population.
• 📈 Descriptive statistics include measures like mean, median, mode, standard deviation, and skewness, offering a detailed view of the sample data.
• 🔮 Inferential statistics are used for hypothesis testing and making predictions about population parameters based on sample data.
• 🧐 Understanding the difference between population and sample is crucial for choosing the right statistical methods and drawing valid conclusions.
• 📐 Descriptive statistics are essential for getting a macro and micro view of the data, spotting potential errors, and informing the choice of inferential methods.
• 🚫 It's important not to rush through descriptive statistics to get to inferential methods, as this can lead to flawed results.
• 📝 When selecting statistical methods, consider the level of measurement and the shape of the data (e.g., normal distribution vs. skewed).
• 🎯 Align the choice of statistical methods with the research questions and hypotheses to ensure the analysis is relevant and meaningful.
• 🔄 Descriptive statistics should be the first step in any analysis, followed by inferential statistics if the research aims to make predictions about the population.
• 🌐 For further exploration of statistical methods, the Grad Coach blog and other resources are recommended for a comprehensive understanding.

Q & A

What is the main focus of the video?

The video focuses on explaining the concept of quantitative data analysis, its methods, and how to choose the right methods for research.

What are the two main branches of quantitative analysis discussed in the video?

The two main branches of quantitative analysis discussed are descriptive statistics and inferential statistics.

What is the purpose of descriptive statistics in research?

Descriptive statistics serve to describe the data set, helping researchers understand the details of their sample.

What is the purpose of inferential statistics in research?

Inferential statistics aim to make inferences about the population based on the findings within the sample.

What are some common descriptive statistical metrics mentioned in the video?

Common descriptive statistical metrics mentioned include the mean, median, mode, standard deviation, and skewness.

What are some common inferential statistical methods discussed in the video?

Some common inferential statistical methods discussed are t-tests, ANOVA, correlation analysis, and regression analysis.

How does the video suggest choosing the right statistical methods for research?

The video suggests considering the type of data collected, the level of measurement, the shape of the data, and the specific research questions and hypotheses.

Why is it important to understand the shape of the data when choosing statistical methods?

Understanding the shape of the data is important because different statistical methods work for different shapes of data, such as symmetrical or skewed distributions.

What is the role of the mean in descriptive statistics?

The mean serves as the mathematical average of a range of numbers, providing a central value for the data set.

What does the median represent in a data set?

The median represents the midpoint in a range of numbers when they are arranged in order, indicating the center of the data set.

What is the significance of standard deviation in understanding a data set?

Standard deviation indicates how dispersed a range of numbers is, showing how close all the numbers are to the mean, thus providing insight into the spread of the data.

Outlines

00:00

🔍 Introduction to Quantitative Data Analysis

The video introduces the topic of quantitative data analysis, emphasizing its complexity and the fear it instills in students. The host, Emma, assures viewers that understanding the basics is achievable and aims to simplify the process. She outlines the video's agenda, which includes explaining what quantitative data analysis is, exploring popular methods, and providing tips to avoid common pitfalls. Emma also invites viewers to subscribe for more research-related content and promotes one-on-one coaching services for dissertation or thesis assistance.

05:05

📊 Understanding Quantitative Data Analysis

This section delves into the definition of quantitative data analysis, which involves analyzing numerical data. It contrasts with qualitative analysis by focusing on data that can be quantified. The video explains the purposes of quantitative analysis: measuring differences between groups, assessing relationships between variables, and testing hypotheses rigorously. It also introduces the backbone of quantitative analysis—statistical methods—and mentions that the video will break down complex jargon into understandable concepts.

10:08

📈 Descriptive and Inferential Statistics

The paragraph explains the two main branches of quantitative analysis: descriptive and inferential statistics. Descriptive statistics are used to describe the sample data, focusing on measures like mean, median, mode, standard deviation, and skewness. Inferential statistics, on the other hand, use sample data to make predictions about the entire population. The distinction between 'population' and 'sample' is crucial for understanding these statistical methods, with the population being the entire group of interest and the sample being a subset of that group from which data is collected.

15:13

📉 Practical Example of Descriptive Statistics

A practical example is provided to illustrate the application of descriptive statistics. The example uses a dataset of body weights of ten people to calculate mean, median, mode, standard deviation, and skewness. The video explains how these statistics provide insights into the dataset's characteristics, such as the average weight and the distribution of weights. It also emphasizes the importance of descriptive statistics for understanding data and informing the choice of inferential statistical methods.

20:16

🧐 Inferential Statistics and Hypothesis Testing

The focus shifts to inferential statistics, which are used to make predictions about a population based on sample data. Common predictions include differences between groups and relationships between variables. The video mentions that the composition of the sample is critical for the validity of inferential statistics. It introduces various inferential statistical methods, including t-tests, ANOVA, correlation analysis, and regression analysis, each with its own assumptions and applications. The video also stresses the importance of understanding the sample's representativeness and the data's normal distribution for accurate inferential analysis.

25:19

🛠 Choosing the Right Quantitative Analysis Methods

The video concludes with guidance on selecting appropriate quantitative analysis methods. It emphasizes considering the type of data (levels of measurement and distribution shape) and research questions or hypotheses. The video advises against forcing a method onto research that doesn't align with data types or research aims. It also recaps the key points covered, including the definition of quantitative data analysis, the roles of descriptive and inferential statistics, and the importance of matching statistical methods with research objectives. The host encourages viewers to engage with the content, seek further information, and consider coaching services for research assistance.

Keywords

💡Quantitative Data Analysis

Quantitative data analysis refers to the systematic examination of numerical data. It is a core component of the video's theme, as it is the focus of the tutorial. The script explains that it involves analyzing data that can be converted into numbers without losing meaning, contrasting with qualitative data analysis which deals with non-numerical data. Examples from the script include analyzing the average height differences between groups or the relationship between weather temperature and voter turnout.

💡Descriptive Statistics

Descriptive statistics are used to summarize and describe the main features of a data set. The video emphasizes their importance in understanding the details of a sample. The script mentions several measures of descriptive statistics, such as mean, median, mode, standard deviation, and skewness, and explains their relevance in providing a macro and micro view of the data set.

💡Inferential Statistics

Inferential statistics are used to make predictions about a population based on sample data. The video script explains that while descriptive statistics focus on the sample, inferential statistics extend findings to the larger population. Examples given in the script include t-tests, ANOVA, correlation analysis, and regression analysis, all of which are used to make inferences about differences between groups or relationships between variables.

💡Population

In statistics, the term 'population' refers to the entire group of interest for a study. The video script uses the term to differentiate between the entire group (population) and the subset of that group that is actually studied (sample). The script clarifies the importance of this distinction by explaining how inferential statistics aim to make predictions about the population based on the sample.

💡Sample

A 'sample' is a subset of the population that is accessible and used for research. The video script explains that while the population is the full group of interest, researchers often work with a sample due to practical limitations. The script uses the analogy of a chocolate cake to illustrate the relationship between the population and the sample, with the sample being a slice of the cake.

💡Mean

The 'mean' is the average of a set of numbers, calculated by summing all the values and dividing by the number of values. The video script describes the mean as a common measure in descriptive statistics, used to understand the central tendency of a data set. It is used in the script's example of body weight data to calculate the average weight of a sample.

💡Median

The 'median' is the middle value in a data set when the numbers are arranged in ascending order. The video script explains that the median can indicate the central tendency of a data set and is used to determine if the data is symmetrically distributed. In the script's example, the median is noted to be similar to the mean, suggesting a symmetrical distribution.

💡Mode

The 'mode' is the value that appears most frequently in a data set. The video script defines the mode and notes that it may not exist if all values occur only once, as in the body weight example provided, where each weight is unique and thus there is no mode.

💡Standard Deviation

Standard deviation is a measure of the amount of variation or dispersion in a set of values. The video script explains that a low standard deviation indicates that the values are close to the mean, while a high standard deviation suggests a wide spread of values. It is used in the script to illustrate the dispersion of body weights in the sample.

💡Skewness

Skewness refers to the asymmetry of the probability distribution of a real-valued random variable. The video script describes skewness as a measure of the symmetry of a data set, indicating whether the data leans to the left or right. A negative skewness value, as mentioned in the script, suggests a slight lean to the left.

💡T-Test

A 't-test' is a statistical method used to determine if there is a significant difference between the means of two groups. The video script introduces t-tests as a way to compare the averages of two groups to assess statistical significance, such as comparing the mean blood pressure of two groups with and without a new medication.

💡ANOVA

ANOVA, or Analysis of Variance, is a statistical test that extends the concept of the t-test to compare the means of three or more groups. The video script describes ANOVA as a way to analyze multiple groups, allowing researchers to understand if there are significant differences between group means beyond just two.

💡Correlation Analysis

Correlation analysis is a statistical method that measures the strength and direction of the relationship between two variables. The video script explains that correlation analysis assesses whether an increase in one variable is associated with an increase or decrease in another variable, using the example of the relationship between average temperature and ice cream sales.

💡Regression Analysis

Regression analysis is a statistical process that estimates the relationships among variables. The video script describes regression analysis as going beyond correlation to understand the cause-and-effect relationship between variables. It helps to determine if one variable actually causes changes in another or if they merely move together.

💡Normal Distribution

Normal distribution, also known as Gaussian distribution, is a probability distribution that is symmetrical and forms a bell curve. The video script mentions normal distribution in the context of data shape, indicating that different statistical methods are appropriate for normally distributed data versus skewed data.

💡Hypothesis Testing

Hypothesis testing is a process of making statistical inferences about a population based on a sample. The video script discusses hypothesis testing in the context of inferential statistics, explaining that it is used to test predictions or hypotheses that anticipate changes or differences.

Highlights

Introduction to the basics of quantitative data analysis, emphasizing its importance and potential complexity.

Explanation of quantitative data analysis as the examination of numerical data, contrasting it with qualitative analysis.

Quantitative analysis is used for measuring group differences, assessing variable relationships, and testing hypotheses.

Descriptive statistics are introduced as methods for describing the sample data, including mean, median, mode, standard deviation, and skewness.

Inferential statistics are explained as methods for making predictions about the population based on sample data.

The importance of understanding the terms 'population' and 'sample' in the context of statistical analysis.

Descriptive statistics are highlighted for their role in understanding data details and informing the choice of inferential methods.

The significance of sample representativeness in making accurate inferences about the population.

T-tests are introduced as a method for comparing the means of two groups to determine statistical significance.

ANOVA is presented as an extension of t-tests for analyzing the means of multiple groups.

Correlation analysis is described for assessing the relationship between two variables.

Regression analysis is explained as a method for understanding the cause-and-effect relationship between variables.

The process of choosing the right statistical methods based on data type, research questions, and hypotheses.

The importance of considering data levels of measurement (nominal, ordinal, interval, ratio) when selecting statistical methods.

The role of descriptive statistics in determining the shape of data and its suitability for different inferential methods.

Recap of the key points covered in the video, emphasizing the practical application of quantitative data analysis in research.

Encouragement for viewers to engage with the content, ask questions, and subscribe for more research-related content.

Introduction of Grad Coach's private coaching service for personalized research assistance.

Transcripts

In this video, we're going to jump into the often  confusing world of quantitative data analysis.  

We're going to explore what quantitative data  analysis is, some of the most popular analysis  

methods and how to choose the right methods for  your research. We'll also cover some useful tips,  

as well as common pitfalls to avoid when  you're undertaking quantitative analysis.  

So grab a cup of coffee, grab a cup of tea,  whatever works for you and let's jump into it!

Hey! Welcome to Grad Coach TV - where we demystify  and simplify the oftentimes intimidating world of  

academic research my name is Emma and today  we're going to unwrap the topic of quantitative  

data analysis if you're new here be sure to hit  that subscribe button for more videos covering  

all things research-related also if you're  looking for hands-on help with your research  

check out our one-on-one coaching services where  we help you through your dissertation thesis  

or research project step by step it's basically  like having a professor in your pocket whenever  

you need it so if that sounds interesting to you  you can learn more and book a free consultation  

with a friendly coach at www all right  with that out of the way let's jump into it

quantitative data analysis is one of those things  that often strikes fear into students it's totally  

understandable quantitative analysis is a complex  topic full of daunting lingo like medians modes  

correlations and regression suddenly we're all  wishing we'd paid a little more attention in math  

class now the good news is that while quantitative  data analysis is a mammoth topic gaining a working  

understanding of the basics isn't that hard even  for those of us who avoid numbers and math at all  

costs in this video we'll break quantitative  analysis down into simple bite-sized chunks  

so you can get comfy with the core concepts  and approach your research with confidence  

so let's start with the most basic question what  exactly is quantitative data analysis despite  

being quite a mouthful quantitative data analysis  simply means analyzing data that's numbers based  

or data that can be easily converted into  numbers without losing any meaning for example  

category based variables like gender ethnicity  or native language can all be converted into  

numbers without losing meaning for example  english could equal one french could equal two  

and so on this contrasts against qualitative data  analysis where the focus is on words phrases and  

expressions that can't be reduced to numbers  if you're interested in learning about  

qualitative analysis we've got a video covering  that as well i'll include a link below so the  

next logical question is what is quantitative  analysis used for well quantitative analysis is  

generally used for three purposes first it's used  to measure differences between groups for example  

average height differences between different  groups of people second it's used to assess  

relationships between variables for example  the relationship between weather temperature  

and voter turnout and third it's used to test  hypotheses in a scientifically rigorous way  

for example a hypothesis about  the impact of a certain vaccine  

again this contrasts with qualitative analysis  which can be used to analyze people's perceptions  

and feelings about an event or situation in other  words things that can't be reduced to numbers  

so how does quantitative analysis work you ask  well since quantitative data analysis is all  

about analyzing numbers it's no surprise that it  involves statistics statistical analysis methods  

form the engine that powers quant analysis these  methods can vary from pretty basic calculations  

for example averages and medians to more  sophisticated analyses for example correlations  

and regressions sounds like a bunch of gibberish  don't worry we will explain all of that in this  

video importantly you don't need to be a  statistician or a math whiz to pull off a  

good quantitative analysis we'll break down  all the technical mumbo jumbo in this video  

so let's start by taking a look at the  two main branches of quantitative analysis

as i mentioned quantitative analysis is powered  by statistical analysis methods there are two main  

branches of statistical methods that are used  descriptive statistics and inferential statistics  

in your research you might only use descriptive  statistics or you might use a mix of both  

depending on what you're trying to figure out in  other words depending on your research questions  

aims and objectives i'll explain how to  choose your methods later in this video  

so what are descriptive and inferential statistics  well before i can explain that we need to take a  

quick detour to explain some lingo to understand  the difference between these two branches  

of statistics you need to understand two  important words these words are population  

and sample first step population in statistics  the population is the entire group of people or  

animals or organizations or whatever that  you're interested in researching for example  

if you were interested in researching tesla  owners in the us then the population would be  

all tesla owners in the united states however  it's extremely unlikely that you're gonna be  

able to interview or survey every single tesla  owner in the u.s realistically you'll only get  

access to a few hundred or maybe a few  thousand owners using an online survey  

this smaller group of accessible people whose  data you actually collect is called your sample  

so to recap the population is the entire group of  people you're interested in and the sample is the  

subset of that population that you can actually  get access to in other words the population is  

the full chocolate cake whereas the sample is just  a slice of that cake can you see what i've got on  

my mind anyhow why is this sample population thing  important well descriptive statistics focuses on  

describing the sample while inferential statistics  aim to make predictions about the population  

based on the findings within the sample in other  words we use one group of statistical methods  

descriptive statistics to investigate the slice  of cake and another group of methods inferential  

statistics to draw conclusions about the entire  cake and there i go with the cake analogy again  

but to be fair i always have chocolate on my  mind so with that out of the way let's take a  

closer look at each of these branches in more  detail starting with descriptive statistics

descriptive statistics serve a simple but  critically important role in your research  

to describe your data set hence the name in other  words they help you understand the details of  

your sample unlike inferential statistics which  we'll get to later descriptive statistics don't  

aim to make inferences or predictions about the  entire population they're purely interested in  

the details of your specific sample when you're  writing up your analysis descriptive statistics  

are the first set of stats you'll cover before  moving on to inferential statistics but depending  

on your research objectives and research questions  they may be the only type of statistics that you  

use we'll explore that a little later so what kind  of statistics are usually covered in this section  

well some common statistical tests used in this  branch include the following the mean this is  

simply the mathematical average of a range of  numbers nothing too complicated here next is the  

median this is the midpoint in a range of numbers  when the numbers are all arranged in order if the  

data set makes up an odd number then the median  is the number right in the middle of the set  

if the data set makes up an even number then  the median is the midpoint between the two  

middle numbers next up is the mode this is simply  the most commonly repeated number in the data set  

then we have standard deviation this metric  indicates how dispersed a range of numbers is  

in other words how close all the numbers are  to the mean the average in cases where most  

of the numbers are quite close to the average  the standard deviation will be relatively low  

conversely in cases where the numbers are  scattered all over the place the standard  

deviation will be relatively high lastly we have  skewness as the name suggests skewness indicates  

how symmetrical a range of numbers is in other  words do they tend to cluster into a smooth bell  

curve shape in the middle of the graph this  is called a normal or parametric distribution  

or do they lean to the left or right this is  called a non-normal or non-parametric distribution  

okay are you feeling a bit confused  let's look at a practical example  

on the left hand side is the data set this data  set details the body weight in kilograms of a  

sample of 10 people on the right hand side we  have the descriptive statistics for this data set  

let's take a look at each of them first we can  see that the mean weight is 72.4 kilograms in  

other words the average weight across the sample  is 72.4 kilograms pretty straightforward next  

we can see that the median is very similar to the  mean the average this suggests that this data set  

has a reasonably symmetrical distribution in other  words a relatively smooth center distribution of  

weights clustered towards the center moving on to  the mode well there is no mode in this data set  

this is because each number presents itself only  once and so there cannot be a most common number  

if hypothetically there were two people who were  both 65 kilograms then the mode would be 65.  

next up is the standard deviation 10.6 indicates  that there's quite a wide spread of numbers we  

can see this quite easily by just looking at the  numbers which range from 55 to 90. this is quite a  

stretch from the mean of 72.4 so we would expect  the standard deviation to be well above zero  

and lastly let's look at the skewness a result  of negative 0.2 tells us that the data is very  

slightly negatively skewed in other words it has  a very slight lean this makes sense since the  

mean and the median are only slightly different as  you can see these descriptive statistics give us  

some useful insight into the data set of course  this is a very small data set only 10 records  

so we can't read into these statistics too much  but hopefully this example helps you understand  

how these statistics play out in reality also keep  in mind that this is not a list of all possible  

descriptive statistics just the most common  ones so at this point you might be wondering  

but why do these matter well while these  descriptive statistics are all fairly basic  

they're important for a few reasons firstly  they help you get both a macro and micro level  

view of your data they help you understand  both the big picture and the finer details  

secondly they help you spot potential errors in  the data for example if an average is way higher  

than you'd expect or responses to a question  are highly varied this can act as a warning  

sign that you need to double check the data and  lastly these descriptive statistics help inform  

which inferential statistical methods you can use  as those methods depend on the shape of the data  

we'll explore this a little bit more later  on simply put descriptive statistics are  

really important even though the  statistical methods used are pretty basic  

all too often at grad coach we see students  rushing past the descriptives in their eagerness  

to get to the more exciting inferential methods  and then landing up with some very flawed results  

don't be a sucker give your descriptive  statistics all the love and attention they deserve

all right now that we've looked at  descriptive stats let's move on to  

the second branch of quantitative  analysis inferential statistics

as i mentioned while descriptive statistics are  all about the details of your specific data set  

your sample inferential statistics aim to make  inferences about the population in other words  

you'll use inferential statistics to make  predictions about what you'd expect to find  

in the full population what kind of predictions  you ask well generally speaking there are two  

common types of predictions that research  try to make using inferential stats firstly  

predictions about differences between groups  for example height differences between children  

grouped by their favorite sport and secondly  relationships between variables for example  

the relationship between body weight and the  number of hours a week a person does yoga  

in other words inferential statistics when  done correctly allow you to connect the  

dots and make predictions about what you'd  expect to see in the real world population  

based on what you observe in your sample data  for this reason inferential statistics are used  

for hypothesis testing in other words to test  hypotheses that predict changes or differences  

of course when you're working with inferential  statistics the composition of your sample is  

really important in other words if your  sample doesn't accurately represent the  

population you're researching then your findings  won't necessarily be very useful for example  

if your population of interest is a mix of 50  male and 50 female but your sample is 80 male  

you can't make inferences about the population  based on your sample since it's not representative  

this area of statistics is called sampling but we  won't go down that rabbit hole here it's a deep  

one we'll save that for another video so what kind  of statistics are usually covered in this section  

well there are many many different statistical  analysis methods within the inferential branch  

and it would be impossible for us to discuss  them all here so we'll just take a look at  

some of the most common inferential statistical  methods so that you have a solid starting point

first up are t-tests t-tests compare the  means the averages of two groups of data  

to assess whether they are different to a  statistically significant extent in other words  

to see whether they have significantly different  means standard deviations and skewness for example  

you might want to compare the mean blood pressure  between two groups of people one that has taken a  

new medication and one that hasn't to assess  whether they are significantly different  

simply looking at the two means  is not enough to draw a conclusion  

you need to assess whether the differences  are statistically significant and that's  

what t-tests allow you to do right next up is  anova anova stands for analysis of variance  

this test is similar to a t-test in that it  compares the means of various groups but anova  

allows you to analyze multiple groups not just  two so it's basically a t-test but on steroids

next we have correlation analysis this type of  analysis assesses the relationship between two  

variables in other words if one variable increases  does the other variable also increase decrease  

or stay the same for example if the average  temperature goes up do average ice cream sales  

increase too we'd expect some sort of relationship  between these two variables intuitively  

but correlation analysis allows us to  measure that relationship scientifically

lastly we have regression analysis regression  analysis is similar to correlation in that it  

assesses the relationship between variables but  it goes a step further to understand the cause  

and effect between variables not just whether they  move together in other words does the one variable  

actually cause the other one to move or do they  just happen to move together naturally thanks to  

another force just because two variables correlate  doesn't necessarily mean that one causes the other  

to make this all a little more tangible let's  take a look at an example of correlation in  

action here's a scatter plot demonstrating the  correlation or the relationship between weight and  

height intuitively we'd expect there to be some  sort of relationship between these two variables  

which is what we see in this scatter plot in other  words the results tend to cluster together in a  

diagonal line from bottom left to top right the  more tightly the results cluster together to form  

a line in any direction the more correlated they  are and therefore the stronger the relationship  

between the variables as i mentioned these are  just a handful of inferential methods there  

are many many more importantly each statistical  method has its own assumptions and limitations  

for example some methods only work with normally  distributed or parametric data while other methods  

are designed specifically for data that are  not normally distributed and that's exactly why  

descriptive statistics are so important they're  the first step to knowing which inferential  

methods you can and can't use of course this  all begs the question how do i choose the right  

quantitative analysis methods for my research  well that's exactly what we'll look at next

now that we've looked at some of the most common  statistical methods used within quantitative  

analysis let's look at how you go about choosing  the right tool for the job to choose the right  

statistical methods for your research you need  to think about two important factors one the  

type of quantitative data you have specifically  level of measurement and the shape of the data  

and two your research questions and hypotheses  let's take a closer look at each of these the  

first thing you need to consider is the type of  data you've collected or the data you will collect  

by data types i'm referring to the four levels  of measurement namely nominal ordinal interval  

and ratio if you're not familiar with this lingo  you should hit the pause button real quick and  

go check out our post over on the grad coach blog  that explains each of these levels of measurement  

i'll include the link below okay so why does this  matter well because different statistical methods  

require different types of data this is one of the  assumptions i mentioned earlier every method has  

its assumptions regarding the type of data for  example some methods work with categorical data  

like yes or no type questions while others work  with numerical data like age weight or income if  

you try to use a statistical method that doesn't  support the data type you have your results will  

be largely meaningless so make sure you have a  clear understanding of what types of data you've  

collected or will collect once you have this you  can then check which statistical methods support  

your data types i'll include a link below the  video that explains which methods support which  

data types now if you haven't collected your data  yet you can of course reverse engineer the process  

and look at which statistical methods would  give you the most useful insights and then  

design your data collection strategy around this  to ensure that you collect the correct data types

another important factor to  consider is the shape of your data  

specifically does it have a normal distribution  in other words is it a bell-shaped curve  

centered in the middle or is it  very skewed to the left or right  

again different statistical methods work for  different shapes of data some are designed  

for symmetrical data while others are designed  for skewed data this is another reminder of why  

descriptive statistics are so important since  they tell you all about the shape of your data

the next thing you need to consider is your  specific research questions as well as your  

hypotheses if you have some the nature of your  research questions and research hypotheses  

will heavily influence which statistical  methods you should use if you're just  

interested in understanding the attributes of your  sample as opposed to the entire population then  

descriptive statistics might be all you need for  example if you just want to assess the means or  

averages and the medians or center points of  variables in a group of people descriptives  

will do the trick on the other hand if you aim  to understand differences between groups or  

relationships between variables and to  infer or predict outcomes in the population  

then you'll likely need both descriptive  statistics and inferential statistics so  

it's really important to get very  clear about your research aims  

and research questions as well as your hypotheses  before you start looking at which statistical  

methods to use never shoehorn a specific method  into your research just because you like it  

or have experience with it your choice of methods  must align with all the factors we've covered here

all right now that we've looked  at what quantitative analysis is  

the two main branches of statistics and how  to choose the right methods for your research  

let's recap and bring it all together

we've covered a lot in this video  well done on making it this far  

let's recap on the key points we've looked at  first we asked the question what is quantitative  

data analysis as we discussed quantitative  analysis is all about analyzing number based data  

which can include both categorical and numerical  data these data are analyzed using statistical  

methods the two main branches of statistics are  descriptive statistics and inferential statistics  

descriptives describe your sample the slice of  the cake while inferentials make predictions  

about what you'll find in the population the full  cake based on what you've observed in the sample  

as we saw common descriptive statistical  metrics include the mean the median the mode  

standard deviation and skewness on the inferential  side we looked at t tests anovas correlation  

analysis and regression analysis all of which can  help you make predictions about the population  

lastly we asked the important question how  do i choose the right statistical methods  

as we discussed to choose the right  statistical methods you need to consider  

the type of data you're working as well  as your research questions and hypotheses  

remember in this video we've only looked at a  handful of the most common quantitative methods  

there are many many more so be sure to check out  the grad coach blog as well as the other links  

below this video to get a fuller picture of what  all's on offer in terms of statistical methods  

also if you'd like us to cover any of the methods  in more detail be sure to leave a comment below

alright that wraps it up for today if you  enjoyed the video hit that like button and  

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